Drug testing is a time consuming, expensive, and at times dangerous proposition. But a new set of computer models has successfully predicted the negative side effects of hundreds of current drugs, a development that could see more efficient development of pharmaceuticals - and without the risks of testing on human and animal subjects.

Pharmaceuticals fail at the clinical-trial phase all the time, mostly on account of their ineffectiveness. They also fail because many of them tend to have rather nasty side effects — and often the only way to know whether or not these proposed drugs have these side effects is to test them on humans or animals.

There are also the costs to consider. It takes about $1 billion dollars and 15 years to bring a drug to market, with some estimates reaching as high as $4 billion to $12 billion per drug. It's the failures that often drive up these costs.

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But a recent breakthrough in computer modeling is looking to change all this. According to a study that recently appeared in the journal Nature, a research team, co-led by researchers in the UCSF School of Pharmacy, Novartis Institutes for BioMedical Research (NIBR) and SeaChange Pharmaceuticals, Inc., put a computer simulation to the test to see if it could help identify drugs that would result in adverse side effects.

To test their models, the researchers took 656 drugs that are currently on the market with known safety records or side effects. The simulation, which analyzes the similarity between chemical structures and those molecules known to cause side effects, was able to successfully predict the negative effects in half of the drugs. While not perfect, the researchers described their breakthrough as "a significant leap forward from previous work", in which they were never able to tackle hundreds of compounds at once.

In terms of specifics, the computer simulation identified 1,241 possible side-effect targets for the 656 drugs, of which 348 were confirmed by a proprietary database of drug interactions. Another 151 hits revealed potential side effects that had never been identified for these drugs, but were later confirmed through lab testing.

For example, researchers have known about a synthetic form of estrogen that has been known to cause stomach pain, but with no known cause. The simulation showed that it binds strongly to a target known as COX-1, which is the protein target of non-steroidal anti-inflammatory drugs, such as aspirin, which also can cause stomach pain, ulceration, and bleeding.

Looking to the future, the researchers describe the potential for a "computerized safety panel" that could not only detect adverse side effects of experimental drugs, but also provide valuable guidance in efforts to repurpose existing drugs for new diseases and conditions.